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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Debugging and Deploying | 10% | - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices |
| Topic 2: Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Topic 3: Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Use Databricks-specific libraries and APIs - Implement complex data processing logic |
| Topic 4: Data Governance | 7% | - Manage data assets and metadata - Enforce data policies and standards - Use Unity Catalog for governance |
| Topic 5: Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
| Topic 6: Data Sharing and Federation | 5% | - Manage cross-platform data access - Implement Lakehouse Federation - Use Delta Sharing for secure data sharing |
| Topic 7: Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions |
| Topic 8: Data Modelling | 6% | - Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture |
| Topic 9: Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Handle incremental and batch data loads - Ingest data from diverse sources |
| Topic 10: Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Track data lineage and metrics - Set up alerts and notifications |
Databricks Certified Data Engineer Professional Sample Questions:
A faulty IoT sensor in a factory reports a temperature of -500, causing the LDP pipeline to fail the expectation, which only allows values between -100 and 200 degrees Celsius. The data engineer would like to further analyze the faulty data to better understand the reason behind this. How should the data engineer resolve the faulty data while ensuring data quality standards are maintained?
- A. Ignore the error and simply re-run the pipeline, as Databricks will automatically skip the problematic record on the next run.
- B. Change the expectation action from fail to warn so that invalid records are included in the output and the pipeline does not fail.
- C. Fix the pipeline code and implement a quarantine logic to isolate the faulty data before re-running the pipeline.
- D. Remove all expectations form the pipeline to prevent any future failures, regardless of data quality.
Correct Answer: C 🗳️
Explanation: Only visible for itPass4sure members. You can sign-up / login (it's free).
What statement is true regarding the retention of job run history?
- A. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
- B. It is retained for 90 days or until the run-id is re-used through custom run configuration
- C. It is retained for 60 days, after which logs are archived
- D. It is retained for 60 days, during which you can export notebook run results to HTML
- E. It is retained until you export or delete job run logs
Correct Answer: D 🗳️
A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?
- A. MANAGE privilege on the finance_data catalog.
- B. GRANT OPTION privilege on the finance_data catalog.
- C. ALL PRIVILEGES on the finance_data catalog.
- D. Make the finance team lead a metastore admin.
Correct Answer: A 🗳️
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A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?
- A. Apply a column mask that references the group_access mapping table in its UDF.
- B. Create a view without selecting the sensitive column.
- C. Use a row filter to restrict access based on the user's group.
- D. Create a UDF that hardcodes allowed groups and apply it as a column mask.
Correct Answer: A 🗳️
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A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve. Which Delta capability should the team leverage on video_events?
- A. Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.
- B. Enable Delta caching.
- C. Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
- D. Enable auto-compaction (optimizeWrite and autoCompact).
Correct Answer: C 🗳️
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